Experimenting the influence of corncob ash on the mechanical strength of slag-based geopolymer concrete

Author:

Wang Jing1,Qu Qian1,Khan Suleman Ayub2,Alotaibi Badr Saad3,Althoey Fadi4,Gamil Yaser5,Najeh Taoufik6

Affiliation:

1. School of Civil Engineering, Chongqing Industry Polytechnic College , No.1000 Taoyuan Avenue, Airport, Yubei District , Chongqing 401120 , China

2. Department of Civil Engineering, COMSATS University Islamabad , Abbottabad 22060 , Pakistan

3. Architectural Engineering Department, College of Engineering, Najran University , Najran , Saudi Arabia

4. Department of Civil Engineering, College of Engineering, Najran University , Najran , Saudi Arabia

5. Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan , 47500 Bandar Sunway , Selangor , Malaysia

6. Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology , Lulea , Sweden

Abstract

Abstract The construction sector has been under growing public attention recently as one of the leading causes of climate change and its detrimental effects on local communities. In this regard, geopolymer concrete (GPC) has been proposed as a replacement for conventional concrete. Predicting the concrete’s strength before pouring is, therefore, quite useful. The mechanical strength of slag and corncob ash (SCA–GPC), a GPC made from slag and corncob ash, was predicted utilizing multi-expression programming (MEP). Modeling parameters’ relative importance was determined using sensitivity analysis. When estimating the compressive, flexural, and split tensile strengths of SCA–GPC with MEP, 0.95, 0.93, and 0.92 R 2-values were noted between the target and predicted results. The developed models were validated using statistical tests for error and efficiency. The sensitivity analysis revealed that within the mix proportions, the slag quantity (65%), curing age (25%), and fine aggregate (3.30%) quantity significantly influenced the mechanical strength of SCA–GPC. The MEP models result in distinct empirical equations for the strength characteristics of SCA–GPC, unlike Python-based models, which might aid industry and researchers worldwide in determining optimal mix design proportions, thus eliminating unneeded test repetitions in the laboratory.

Publisher

Walter de Gruyter GmbH

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